A comparison of fuzzy approaches to e-commerce review rating prediction

Acampora, G. ORCID: 0000-0003-4082-5616 and Cosma, G. ORCID: 0000-0002-4663-6907, 2015. A comparison of fuzzy approaches to e-commerce review rating prediction. In: J.M. Alonso, H. Bustince and M. Reformat, eds., Proceedings of IFSA-EUSFLAT 2015, 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Gijón, Asturias, Spain, 30 June - 3 July 2015. Amsterdam: Atlantis Press, pp. 1223-1230. ISBN 9789462520776

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This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor.

Item Type: Chapter in book
Creators: Acampora, G. and Cosma, G.
Publisher: Atlantis Press
Place of Publication: Amsterdam
Date: 2015
ISBN: 9789462520776
ISSN: 1951-6851
Divisions: Schools > School of Science and Technology
Record created by: EPrints Services
Date Added: 09 Oct 2015 10:14
Last Modified: 09 Jun 2017 13:23
URI: https://irep.ntu.ac.uk/id/eprint/9904

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